How do you make large language models smaller and faster—without sacrificing quality? Use a novel approach – Self-Data Distilled Fine-Tuning (Self-Data FT) that Uses the unpruned model to generate a distilled dataset, preserving knowledge. Retains 91.2% accuracy on
Self-Data Distilled Fine-Tuning: Smaller LLMs Without Quality Loss
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